The ongoing expansion and addition of new features in software development bring inefficiency and vulnerabilities into programs, resulting in an increased attack surface with higher possibility of exploitation. Creating customized software systems that contain just-enough features and yet satisfy specific user needs is currently an extremely slow, build-to-order process. In this paper, we propose MORPH, an Interactive Program Feature Customization framework to provide broad capabilities for automated program feature identification and feature customization. Our preliminary results show that MORPH can identify program features at an average accuracy of 92.7% and swiftly generate variations of self-contained, customized programs in an unsupervised fashion.

The backpressure algorithm is known to provide throughput optimality in routing and scheduling decisions for multi-hop networks with dynamic traffic. The essential assumption in the backpressure algorithm is that all nodes are benign and obey the algorithm rules governing the information exchange and underlying optimization needs. Nonetheless, such an assumption does not always hold in realistic scenarios, especially in the presence of security attacks with intent to disrupt network operations. In this paper, we propose a novel mechanism, called virtual trust queuing, to protect backpressure algorithm based routing and scheduling protocols against various insider threats. Our objective is not to design yet another trust-based routing to heuristically bargain security and performance, but to develop a generic solution with strong guarantees of attack resilience and throughput performance in the backpressure algorithm. To this end, we quantify a node's algorithm-compliance behavior over time and construct a virtual trust queue that maintains deviations of a give node from expected algorithm outcomes. We show that by jointly stabilizing the virtual trust queue and the real packet queue, the backpressure algorithm not only achieves resilience, but also sustains the throughput performance under an extensive set of security attacks. Our proposed solution clears a major barrier for practical deployment of backpressure algorithm for secure wireless applications.

This paper addresses the problem of joint inference and optimization in wireless networks. An optimization framework based on information-geometric network inference is developed and implemented (using a real radio emulation testbed) with scalable solutions to infer the end-to-end rate distributions of stochastic network flows from link rate measurements. The proposed low-complexity solutions apply when the underlying network inference (network tomography) problem can be decomposed to smaller-size subproblems that are solved independently by partially inferring only the flow rates of interest. The solutions are extended to infer flow rates jointly with link loss rates when retransmissions are considered over unreliable wireless links. By using the inferred distributions of flow rates, an inference mechanism is presented to optimize the network performance. First, the distributions of flow rates are inferred from the average rate measurements on selected links. Then, the distributions of all links rates are computed from the inferred distributions of flow rates. Finally, these inference results are used with network optimization. The weighted sum of link outage probabilities is minimized by adapting power control or routing decisions in mobile wireless access. This approach is iterated between network inference and optimization, providing link outage and end-to-end throughput gains compared to static approaches with fixed network (inference and optimization) parameters. The joint network inference and optimization framework is implemented with real configurable radios and the performance is verified with hardware-in-the-loop emulation test results that are obtained with actual radio transmissions over emulated channels.

The backpressure algorithm is known to provide throughput optimality in routing and scheduling decisions for multi-hop networks with dynamic traffic. The essential assumption in the backpressure algorithm is that all nodes are benign and obey the algorithm rules governing the information exchange and underlying optimization needs. Nonetheless, such an assumption does not always hold in realistic scenarios, especially in the presence of security attacks with intent to disrupt network operations. In this paper, we propose a novel mechanism, called virtual trust queuing, to protect backpressure algorithm based routing and scheduling protocols from various insider threats. Our objective is not to design yet another trust-based routing to heuristically bargain security and performance, but to develop a generic solution with strong guarantees of attack resilience and throughput performance in the backpressure algorithm. To this end, we quantify a node's algorithm-compliance behavior over time and construct a virtual trust queue that maintains deviations from expected algorithm outcomes. We show that by jointly stabilizing the virtual trust queue and the real packet queue, the backpressure algorithm not only achieves resilience, but also sustains the throughput performance under an extensive set of security attacks.

We study deadline-aware scheduling with adaptive network coding (NC) for real-time traffic over a single-hop wireless network. To meet hard deadlines of real-time traffic, the block size for NC is adapted based on the remaining time to the deadline so as to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. This sequential block size adaptation problem is then cast as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and that the optimal block size is bounded by the “greedy” block size. These unique structures make it possible to significantly narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can find the optimal block size in polynomial time. Furthermore, a joint real-time scheduling and channel learning scheme with adaptive NC is developed to adapt to channel dynamics in a mobile network environment. Then, we generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints. We devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotical utility optimality. The analysis and simulation results are corroborated by high-fidelity wireless emulation tests, where actual radio transmissions over emulated channels are performed to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.

A fundamental challenge in wireless networks is how to handle packet loss due to noise, interference, and dynamic channel effects, especially when there is no per-packet acknowledgement due to additional delay and potential loss of feedback packets. We design and implement a packet coding optimization scheme, applied at the source node, to enhance end-to-end transmission reliability in a lossy multi-hop network. Specifically, each source node transmits a file of packets to its destination node in the network where the end-to-end packet acknowledgement is not readily available because of the possible error or delay effects over multiple hops. By simply retransmitting packets, a source node cannot guarantee innovative packet arrivals at the destination node. Thus, we design an optimization scheme for adaptive packet coding applied at the source node to avoid transmitting redundant packets, thereby significantly improving the throughput, even for the case of a single unicast session. This scheme does not require any knowledge of network topology and can seamlessly operate with any routing or network coding protocol used at the intermediate relay nodes. We analyze the throughput properties of coded transmissions and verify the feasibility of performance gains via simulations. Then, we provide high fidelity emulation testbed results with real radio transmissions over emulated channels to evaluate the throughput gains. Without relying on end-to-end acknowledgment for each packet, we show that the adaptive packet coding optimization scheme can achieve significantly higher throughput than the retransmission scheme in lossy networks with unicast traffic.

In this demonstration, we present new capabilities of Intelligent Automation Inc. (IAI) Radio Frequency Network Emulation Simulation Tool (RFnestTM) (www.i-a-i.com/rfnest). RFnestTM is a Field Programmable Gate Array (FPGA) based network channel emulator that allows all of the channels of a full network of radio nodes to be emulated in real time, with all communication nodes experiencing a realistic channel impulse response. This allows RFnestTM to be used for protocol testing, replaying field tests, and model validation. RFnestTM has a modular design with three main capabilities: 1) FPGA based emulation hardware with RF front ends that allows nodes with real radios to send their RF signal over an emulated channel without any modification to the radio, 2) modeling of time-varying channel impulse responses within the emulation hardware, with channel properties based on mobility defined with a scripted or interactive Graphic User Interface (GUI) environment, and 3) integration with network emulators and monitoring functionality that allows the user to instantiate, manage, and monitor real and virtual network nodes within the scenario. In the new version of RFnestTM we now support: Frequency programmability: The new RFnestTM allows users to change the center frequency through software from 800 Mhz to 2.7GHz. Increased bandwidth: RFnestTM supports a bandwidth of 60 MHz. Higher fidelity channel modeling: Through software and hardware updates RFnestTM support wireless channels with up to 20 taps and a wide range of Doppler frequencies. MIMO: RFnestTM now supports different combination of MIMO channel between transmitters and receivers.

Nanowire field-effect transistors (nano-FETs) are nano devices capable of highly sensitive, label-free sensing of molecules. However, significant variations in sensitivity across devices can result from poor control over device parameters, such as nanowire diameter and the number of electrode-bridging nanowires. This paper presents a fabrication approach that uses wafer-scale nanowire contact printing for throughput and uses automated nanomanipulation for precision control of nanowire number and diameter. The process requires only one photolithography mask. Using nanowire contact printing and post processing (i.e., nanomanipulation inside scanning electron microscope), we are able to produce devices all with a single nanowire and similar diameters at a speed of ∼1 min/device with a success rate of 95% (n=500). This technology represents a seamless integration of wafer-scale microfabrication and automated nanorobotic manipulation for producing nano-FET sensors with consistent response across devices.

We study adaptive network coding (NC) for scheduling real-time traffic over a single-hop wireless network. To meet the hard deadlines of real-time traffic, it is critical to strike a balance between maximizing the throughput and minimizing the risk that the entire block of coded packets may not be decodable by the deadline. Thus motivated, we explore adaptive NC, where the block size is adapted based on the remaining time to the deadline, by casting this sequential block size adaptation problem as a finite-horizon Markov decision process. One interesting finding is that the optimal block size and its corresponding action space monotonically decrease as the deadline approaches, and the optimal block size is bounded by the "greedy" block size. These unique structures make it possible to narrow down the search space of dynamic programming, building on which we develop a monotonicity-based backward induction algorithm (MBIA) that can solve for the optimal block size in polynomial time. Since channel erasure probabilities would be time-varying in a mobile network, we further develop a joint real-time scheduling and channel learning scheme with adaptive NC that can adapt to channel dynamics. We also generalize the analysis to multiple flows with hard deadlines and long-term delivery ratio constraints, devise a low-complexity online scheduling algorithm integrated with the MBIA, and then establish its asymptotic throughput-optimality. In addition to analysis and simulation results, we perform high fidelity wireless emulation tests with real radio transmissions to demonstrate the feasibility of the MBIA in finding the optimal block size in real time.

We consider network tomography and monitoring in dynamic wireless systems and leverage analytical tools from optimization theory and information geometry to infer the invariant statistical network structures. We extend the classical network tomography problem beyond average link rate measurements and develop a systematic optimization mechanism to infer the end-to-end wireless network behavior. This involves estimating the distributions of global network flow rates from the arbitrary statistics collected for the wireless link (channel) rates subject to the topology and link capacity constraints. We develop first a centralized network inference framework based on minimizing the distance of network flow rates from the prior information in the probability space that is spanned by the measurement constraints. Then, distributed implementation follows from message passing among the individual probes in the network and balances the complexity and convergence trade-offs. This formulation facilitates multi-scale multi-resolution inference of flow rates along with link capacity estimation. The underlying optimization framework for information-geometric network inference adapts to wireless network dynamics and offers robust operation with respect to the measurement errors and conflicts as well as the temporal and spatial variations in wireless networks.

It is known that distributed power control in wireless ad-hoc networks is challenging, due to the inherent global coupling between concurrent transmissions interfering with each other. Observing that the globally optimal point lies on the boundary of the feasible region, we transform the utility maximization problem into a more structured problem in the form of maximizing the minimum weighted utility. Then, we develop a centralized algorithm for the minimum weighted utility maximization problem as a benchmark. Next, by using extended duality theory, we introduce penalty multipliers and decompose the minimum weighted utility maximization problem into subproblems for individual users. Appealing to the simulated annealing method, we propose a distributed stochastic power control algorithm, where each user stochastically adjusts its target utility to improve the overall system utility. Although the underlying optimization problem is nonconvex, our algorithm can guarantee global optimality although the convergence rate may be slow due to the usage of simulated annealing. We improve the convergence rate further by devising an enhanced algorithm based on the geometric cooling schedule.

We consider a cognitive radio network where primary users (PUs) employ network coding for data transmissions. We view network coding as a spectrum shaper, in the sense that it increases spectrum availability to secondary users (SUs) and offers more structure of spectrum holes, which in turn improves the predictability of the primary spectrum. With this spectrum shaping effect of network coding, each SU can carry out adaptive channel sensing by dynamically updating the list of the (predicted) idle PU channels and giving priority to these channels for spectrum sensing. This dynamic spectrum access approach with network coding improves how SUs detect and utilize spectrum holes over PU channels. Our results show that compared to the existing approaches based on retransmission, both PUs and SUs can achieve higher stable throughput, thanks to the spectrum shaping effect of network coding.

Capturing the uncertain aspects in cyber security is important for security analysis in enterprise networks. However, there has been insufficient effort in studying what modeling approaches correctly capture such uncertainty, and how to construct the models to make them useful in practice. In this paper, we present our work on justifying uncertainty modeling for cyber security, and initial evidence indicating that it is a useful approach. Our work is centered around near real-time security analysis such as intrusion response. We need to know what is really happening, the scope and severity level, possible consequences, and potential countermeasures. We report our current efforts on identifying the important types of uncertainty and on using Bayesian networks to capture them for enhanced security analysis. We build an example Bayesian network based on a current security graph model, justify our modeling approach through attack semantics and experimental study, and show that the resulting Bayesian network is not sensitive to parameter perturbation.

Prior measurement and analysis have shown that the traffic in data networks is better described by heavy-tailed (HT) distributions. For ad hoc routing, it is important to understand the impact of the heavy-tailed nature of network traffic for routing decisions since heavy-tailed workload can lead to severe performance degradation. In this paper, we present our current work on ad hoc routing by exploiting the heavy-tailed nature of the session (flow) lifetime. In particular, long- and shortlived flows are differentiated based on the unique predictability nascent in heavy-tailed distributions, with the long-lived flows distributed across the network as disperse as possible, and the short-lived flows treated as "filler" traffic. We have implemented such ideas in various ad hoc routing protocols, and in this paper we present our results experimenting on top of dynamic source routing (DSR). Simulation results reveal that our modified protocol, called DSR-HT, significantly outperforms the state-of- the-art under heavy-tailed workload.

Extensive measurement and analysis have shown that traffic in data networks is better described by heavy-tailed distributions. We present our current work on ad hoc routing by exploiting the heavy-tailed nature of the flow lifetime. In particular, the long- and short-lived flows are differentiated based on the unique predictability nascent in heavy- tailed distributions, with the long- and short-lived flows being handled separately. A modified AODV has been implemented to enable heavy-tailedness awareness. Simulation results reveal that the new protocol, AODV-HT, significantly outperforms the state-of-the-art under heavy-tailed workload.